140-year daily ensemble streamflow reconstructions over 661 catchments in France
Abstract. The recent development of the FYRE climate (French Hydroclimate REanalysis), a high-resolution ensemble daily reanalysis of precipitation and temperature covering the period 1871–2012 and the whole of France, offers the opportunity to derive streamflow series over the country from 1871 onwards. The FYRE Climate dataset has been used as input for hydrological modelling over a large sample of 661 near-natural French catchments using the GR6J lumped conceptual model. This approach led to the creation of the 25-member hydrological reconstructions HyDRE spanning the 1871–2012 period. Two sources of uncertainties have been taken into account: (1) the climate uncertainty by using forcings from all 25 ensemble members provided by FYRE Climate, and (2) the streamflow measurement error by perturbing observations used during the calibration. The hydrological model error based on the relative discrepancies between observed and simulated streamflow has been further added to derive the HydREM streamflow reconstructions. These two reconstructions are compared to other hydrological reconstruction with different meteorological inputs, hydrological reconstructions from machine learning algorithm and independent/dependent observations. Overall the results show the added value of the HydRE and HydREM reconstructions in terms of quality, uncertainty estimation, and representation of extremes, therefore allowing to better understand the variability of past hydrology over France.
Alexandre Devers et al.
Status: final response (author comments only)
- RC1: 'Comment on hess-2023-78', Anonymous Referee #1, 02 May 2023
- RC2: 'Comment on hess-2023-78', Anonymous Referee #2, 04 May 2023
Alexandre Devers et al.
Alexandre Devers et al.
Viewed (geographical distribution)
This paper by Devers et al presents a valuable dataset of reconstructed streamflow across France for the extended period 1871-2012 using the GR6J model. The results show the integrity of the dataset compared to other available datasets in France, as well as the added value of the HydREM deterministic set. The paper presents a novel approach to uncertainty estimation, and is within the scope of the HESS journal. The paper is well structured, and makes good use of graphics.
Please not that I did not follow the intricacies of the maths in sections 3.5.2 to 3.5.4 so I can’t comment on the validity of the approach. I have included grammatical corrections in the attached PDF.
Q1 It is a shame the dataset can’t be extended up to the present for consistency and longevity. Is there a current climate dataset that follows on consistently FYRE that could enable this?
Q2 Does fixing the CemaNeige parameters to the median of the “snowy” catchments for the non-snowy catchments make sense? Should the values not be set to something to indicate there is less snow here? Or should the module not be “switched off”?
Q3 is sampling the observational error randomly a justifiable choice? Would that not affect the variability of the timeseries? Is measurement error not likely to be systematically over and under for periods of time longer than one day? Is there any literature on this?
Q4 you chose 25 associations randomly and then compared them with all 625 for 3 catchments. What tests did you do to show that the differences were not significant. Would bootstrapping not have been a better test?
Q5 looks to me from Fig 2 that you would benefit from breaking Q<1 into more than 1 residual group, but you don’t actually use this?
Q6 Fig 8 it doesn’t look like the reconstructions follow the multidecadal variations well at all pre 1970. I think more discussion is needed on this.
Q7 Fig 10 is interesting, but can an example with reliable observations be found to better demonstrate the validity of HydRE and HydREM? Here you are stating that HydRE and HydREM are “more realistic”, but this is purely subjective based on the available information.
Q8 the reconstructions reviewed here are all using the GR6J model I believe (except GRUN, which is only reviewed in the multi-decadal variability study). Model uncertainty has not really been commented on. A completely unrelated catchment model could produce quite different results. A vast number of studies have shown the large impact of hydrology model uncertainty, which comes in addition to climate input uncertainty, parameter uncertainty and observation uncertainty. This should definitely be discussed in section 5.2 at the very least. Unless this is somewhat accounted for in the error model maths that I did not fully understand…